Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.     

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.     

Structural analysis of the neuropeptide substance P by using vibrational spectroscopy

Substance P (SP) is one of the most studied peptide hormones and knowing the relationship between its structure and function may have important therapeutic applications in the treatment of a variety of stress-related illnesses. In order to obtain a deeper insight into its folding, the effects of different factors, such as pH changes, the presence of Ca²⁺ ions, and the substitution of the Met-NH₂ moiety in the SP structure, was studied by Raman and infrared spectroscopies. SP has a pH-dependent structure. Under acidic–neutral conditions, SP possesses a prevalent β-sheet structure although also other secondary structure elements are present. By increasing pH, a higher orderliness in the SP secondary structure is induced, as well as the formation of strongly bound intermolecular β-strands with a parallel alignment, which favour the self-assembly of SP in β-aggregates. The substitution of the Met-NH₂ moiety with the acidic functional group in the SP sequence, giving rise to a not biologically active SP analogue, results in a more disordered folding, where the predominant contribution comes from a random coil. Conversely, the presence of Ca²⁺ ions affects slightly but sensitively the folding of the polypeptide chain, by favouring the α-helical content and a different alignment of β-strands; these are structural elements, which may favour the SP biological activity. In addition, the capability of SERS spectroscopy to detect SP in its biologically active form was also tested by using different metal nanoparticles. Thanks to the use of silver NPs prepared by reduction of silver nitrate with hydroxylamine hydrochloride, SP can be detected at very low peptide concentration (~ 90 nM). However, the SERS spectra cannot be obtained under alkaline conditions since both the formation of SP aggregates and the lack of ion pairs do not allow a strong enough interaction of SP with silver NPs.

Graphical abstract

     

Prediction of properties of biodiesel-diesel blends using spectrofluorimetry and multivariate calibration

Regressions based on fluorescence spectroscopy were developed to provide relatively inexpensive and rapid measurements of the concentration, viscosity, and specific gravity of biodiesel-diesel blends. The methods involved obtaining a mathematical model from spectrofluorimetric data and data from a given property (concentration, dynamic viscosity, or specific gravity) using partial least squares (PLS) regression, which was then applied as a model for predicting properties of interest. The predicted concentrations, dynamic viscosities, and specific gravities of the biodiesel-diesel blends were compared with actual values and agreed reasonably well with the obtained results. The models showed high correlation between real and predicted values. The R-square values near 1 indicated excellent model accuracy for predicting concentrations, specific gravities, and dynamic viscosities of biodiesel-diesel blends. The residual distribution did not follow a trend with respect to the predicted variables, indicating an excellent fit to the data.      

Reflection and transmission mid-infrared spectroscopy for rapid determination of coal properties by multivariate analysis

In the present paper, the influence of different acquisition techniques (transmission, diffuse reflectance infrared Fourier transform and attenuated total reflectance) in the determination of nine coal properties related to combustion power plants has been studied. Raw coal samples of different origins were pooled for developing a correlation between the resultant spectra and the corresponding coal properties by multivariate analysis techniques. Thus, the existent collinearity in mid-infrared coal spectra led to the application of partial least squares regression (PLS), studying simultaneously the influence of different spectroscopic units as well as several spectral data mathematical pre-treatments. On the other hand, a principal component analysis (PCA) revealed a relationship between principal components and coal composition in both transmission and reflection techniques. Although the best accuracy and precision results were obtained for coal properties related to organic matter, the system was also able to differentiate coal samples attending to the presence of a specific mineral matter, kaolinite.     

Reactivity of dissolving pulp: characterisation using chemical properties, NMR spectroscopy and multivariate data analysis

The reactivity of dissolving pulp was experimentally determined in termsof residual cellulose in viscose. The correlations between 11 chemicalproperties of pulp and filter values and residual cellulose contents of viscosewere then investigated by multivariate data analysis. Both the viscose filtervalue and the residual cellulose were well modelled from the 11 propertiesby partial least squares regression. The results show that pulps with highacetone extractable fractions, high magnesium contents, low alkali resistanceand low viscosity, gave low viscose filter values and low residual cellulosecontents. Pulps with low residual cellulose contents also had low carboxylgroupcontents and low polydispersity. The results are interpreted as that in pulpwith high reactivity, the hemicellulose content is low and that the cellulosechains are shorter and more soluble in alkali. An explanation of the positiveeffect from the high extractive content is that the extractives facilitate thediffusion of carbon disulfide. A principal component analysis of CP/MAS¹³C-NMR spectral data of six pulp samples showed that differences inreactivity between the pulps could be explained by variations in the hydrogenbonds in the cellulose and/or changes in the glucosidic bonds. In a separatestudy electron beam processing enhanced the reactivity, i.e. lowered theresidual cellulose content, of the investigated pulps. The magnitude of theelectron dose, within the tested range (5.4–23.7 kGy), didnotseem to be important, but the reactivity within pulp sheets tended to be ratherinhomogeneous.     

Determination of amylose content in starch using Raman spectroscopy and multivariate calibration analysis

Fourier transform Raman spectroscopy and chemometric tools have been used for exploratory analysis of pure corn and cassava starch samples and mixtures of both starches, as well as for the quantification of amylose content in corn and cassava starch samples. The exploratory analysis using principal component analysis shows that two natural groups of similar samples can be obtained, according to the amylose content, and consequently the botanical origins. The Raman band at 480 cm^?1, assigned to the ring vibration of starches, has the major contribution to the separation of the corn and cassava starch samples. This region was used as a marker to identify the presence of starch in different samples, as well as to characterize amylose and amylopectin. Two calibration models were developed based on partial least squares regression involving pure corn and cassava, and a third model with both starch samples was also built; the results were compared with the results of the standard colorimetric method. The samples were separated into two groups of calibration and validation by employing the Kennard-Stone algorithm and the optimum number of latent variables was chosen by the root mean square error of cross-validation obtained from the calibration set by internal validation (leave one out). The performance of each model was evaluated by the root mean square errors of calibration and prediction, and the results obtained indicate that Fourier transform Raman spectroscopy can be used for rapid determination of apparent amylose in starch samples with prediction errors similar to those of the standard method.

Non-destructive dating of fiber-based gelatin silver prints using near-infrared spectroscopy and multivariate analysis

An innovative approach to date fiber-based gelatin silver prints using near-infrared spectroscopy (NIR) and multivariate analysis is presented. NIR spectra were acquired for 152 film stills printed in the USA between 1914 and 1986, and partial least square (PLS) analysis was used to correlate the spectra with the year the photographs were printed. Principal component analysis and spectral interpretation helped clarify the underlying correlation between the print date and the composition and ageing of the photographic papers. The method was successfully validated with an independent set of 66 film stills printed in the USA, and a prediction error (root mean square error of prediction) of 6 years was achieved. The method was also tested on films stills printed in Germany and Russia, as well as amateur prints and photographs in the collection of the Museum of Modern Art. The prediction error was significantly larger, with the exception of the amateur prints, due to differences in the composition and/or properties of the papers depending on their geographical origin and purpose as confirmed by discriminant analysis.     

Investigation of solid surfaces modified by Langmuir-Blodgett monolayers using sum-frequency vibrational spectroscopy and X-ray photoelectron spectroscopy

Langmuir-Blodgett (LB) monomolecular layers of alkylhydroxamic acids and alkylphosphonic acids on copper and iron substrates have been studied by X-ray photoelectron spectroscopy (XPS) and sum-frequency vibrational spectroscopy. According to the XPS results, the structures of the hydroxamic acid and phosphonic acid Langmuir-Blodgett films are very similar: the thickness of the layer of the hydrocarbon tails is typically 1.9-2.1 nm, while the layer of headgroups is about 0.3-0.35 nm thick. The tilt angle of the carbon chains is estimated to be 20-30 degrees with respect to the sample surface normal, and the molecules are connected to the substrate via their headgroups. Analysis of the P 2p and N 1s lines indicates the presence of deprotonated headgroups. The substrate Cu 2p line includes a component which can be assigned to Cu(2+) ions in a thin Cu(OH)(2) layer. The deposition of LB layers led to significant decrease of the hydroxide-related signal, which indicates that binding of the headgroups to the surface is accompanied by the elimination of water molecules. The sum-frequency spectra also clearly indicate that well-ordered monolayers can be formed by the Langmuir-Blodgett technique. Since the non-resonant background from the metal substrates renders the analysis of the spectra more difficult, model system samples on glass were prepared. It was found that the alkyl chains of the adsorbed acids predominantly adopt the all-trans conformation and form an ordered structure. Upper limits for the mean tilt angle of the terminal methyl groups are approximately 10-20 degrees.     

Determination of conformational preferences of dipeptides using vibrational spectroscopy

The NMR coupling constants ((3)J(H(N), H(alpha))) of dipeptides indicate that the backbone conformational preferences vary strikingly among dipeptides. These preferences are similar to those of residues in small peptides, denatured proteins, and the coil regions of native proteins. Detailed characterization of the conformational preferences of dipeptides is therefore of fundamental importance for understanding protein structure and folding. Here, we studied the conformational preferences of 13 dipeptides using infrared and Raman spectroscopy. The main advantage of vibrational spectroscopy over NMR spectroscopy is in its much shorter time scale, which enables the determination of the conformational preferences of short-lived states. Accuracy of structure determination using vibrational spectroscopy depends critically on identification of the vibrational parameters that are sensitive to changes in conformation. We show that the frequencies of the amide I band and the A12 ratio of the amide I components of dipeptides correlate with the (3)J(H(N), H(alpha)). These two infrared vibrational parameters are thus analogous to (3)J(H(N), H(alpha)), indicators for the preference for the dihedral angle phi. We also show that the intensities of the components of the amide III bands in infrared spectra and the intensities of the skeletal vibrations in Raman spectra are indicators of populations of the P(II), beta, and alpha(R) conformations. The results show that alanine dipeptide adopts predominantly a PII conformation. The population of the beta conformation increases in valine dipeptides. The populations of the alpha(R) conformation are generally small. These data are in accord with the electrostatic screening model of conformational preferences.     

Characterization of PVDF membranes by vibrational spectroscopy

In order to investigate the effectiveness of vibrational spectroscopy in the characterization of polymeric membranes, several poly(vinylidene fluoride) (PVDF) membranes with different porous structures were prepared by the phase inversion process using different casting solvents. An accurate analysis of the Fourier transform Raman (FT-Raman) and the Fourier transform infrared (FTIR) spectra was performed for each sample and the scanning electron microscopy (SEM) results were noted. To highlight the specific problems related to porosity and surface roughness in the acquisition of spectra by different sampling techniques, the attenuated total reflection (ATR) and photoacoustic spectroscopy (PAS) spectra were compared with corresponding spectra obtained from dense films. A detailed analysis of these spectra highlighted their ability in determining the differences in the polymer structure between the two membrane sides. This indicates that (considering the results given by all the different techniques) thorough qualitative membrane characterization can generally be achieved. Moreover, the good quality spectra of the PVDF membrane provide information on a portion of material which depends on its structure, highlighting the usefulness of FTIR-PAS in studying porous materials which, as a rule, give low quality infrared spectra when other sampling techniques are used. However, the complex and inhomogeneous structure of these materials can make quantitative analysis more, or less, difficult.     

燃料特性对车用柴油机有害排放的影响

研究了车用柴油机燃用不同品质燃油时,其排气烟度、颗粒PM、氮氧化物NO_x、碳氢HC和一氧化碳的排放特性,采用了五种不同硫含量、芳烃含量和十六烷值的柴油,进行了发动机台架实验和模拟整车NEDC循环实验。结果表明,随着燃油硫含量的减少,柴油机排气烟度、HC、CO、SO₂排放有所下降;模拟整车NEDC循环的PM排放显著降低;NO_x排放的变化幅度很小。随着燃油芳烃含量的降低,柴油机排气烟度、PM、NO_x、HC、CO排放的降幅显著。随着燃油十六烷值的升高,柴油机的排气烟度大都呈持续下降趋势;PM、HC排放显著降低;NO_x、CO排放的变化幅度较小。     

A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis

Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R² and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R²-values above 85% some of them with RMSEP values significantly smaller than those obtained with FTIR-ATR and FTNIR. The ANN/FT-Raman and ANN/FTIR-ATR1 models were able to estimate the total sulfur content of diesel with 0.01% (w/w) accuracy.     

Near infrared spectroscopy and multivariate analysis to evaluate wheat flour doughs leavening and bread properties

A mixture design of experiment approach was followed to explore formulation effects on the technological properties of wheat flours optimized for industrial bread-making purposes. Ten different flour mixtures were investigated by means of near infrared spectroscopy (NIRS) to obtain information on flour performance in a critical phase such as dough leavening. For each mixture, a laboratory-scale bread making experiment was carried out according to a standardized recipe and the leavening phase of each dough sample was monitored by means of NIRS at different times. Parallel factor analysis (PARAFAC) was used to highlight the existence of differences among the mixtures on the basis of NIR spectrum variability with respect to the leavening time. Additionally, the relationship among the 3-way NIR dataset and some parameters measured on the baked bread loaves (dimensions, volume, weight) was investigated by means of the n-way extension of partial least squares regression (nPLS), in order to evaluate product properties from its leavening step and mixture formulation. The results give better insight on the relationships among wheat flour formulation and its performance in the leavening phase and as far as some properties of the final product are concerned, thus offering a way to monitor the leavening phase and give information on its influence on the final product properties.     

Varietal discrimination of Australian wines by means of mid-infrared spectroscopy and multivariate analysis

This study outlines the use of mid-infrared (MIR) spectroscopy combined with principal component analysis (PCA) and linear discriminant analysis (LDA) for the varietal classification of commercial red and white table wines. Three red varieties (Cabernet Sauvignon, Shiraz and Merlot) and four white varieties (Chardonnay, Riesling, Sauvignon Blanc and Viognier) were sourced from different wine regions in Australia. Wine samples were scanned in transmission on a FOSS WineScan FT 120 from wave numbers 926 to 5012 cm⁻¹. All samples were sourced from the 2006 vintage and had not been blended with any other variety or wine from other regions. Spectral data were reduced to a small number of principal components (PCs) and LDA was then performed to successfully separate the wines into the different varieties. To test the robustness of the LDA models developed for the red wines, a set of red wines scanned in 2005 were used. Correct classification of over 95% was achieved for the validation set.     

States of molecular assembly and physical properties of crystalline long-chain compounds studied by vibrational spectroscopy

Various types of molecular assembly of long-chain compounds in solid states were investigated by means of infrared absorption, Raman and Brillouin spectroscopic methods. As for the polymorphism in even-numbered n-fatty acids, three monoclinic modifications, B, C, and E, all consisting of the orthorhombic polyethylene sublattice, give rise to their characteristic infrared and Raman spectra. A dynamical equilibrium between cis and trans conformations of the hydrogen-bonded carboxyl groups in modification C, which is related to the high-temperature stable character of this phase, is reflected to a dramatic change with temperature in the low-frequency Raman spectra. A new type of reversible solid state phase transition was found between two A-type (triclinic) modifications of myristic, palmitic, and stearic acids. The γ→α phase transition of oleic acid was found to be caused by a conformational disordering of polymethylene chains at the lamellar interfacial region. Two basic polytype structures, Mon and Orth II, of stearic acid B were investigated, and it was found that the low-frequency phonon frequencies (below 50 cm⁻¹) were strongly influenced by the polytype structure. Based on the spectroscopic considerations, Orth II was predicted as the thermodynamically stable phase around room temperature compared with Mon, and the stability is responsible for the vibrational free energy term. Some experimental findings which support this prediction were obtained. The values of the stiffness tensor elements of Mon and Orth II, measured by Brillouin scattering, indicate that the mechanical behavior of bulk crystals is very dependent on the polytype structure. The relationship between the mobility of chain molecules and the width of the spectral bands was investigated in a quantitative manner for the case of n-alkane molecules entrapped in the urea inclusion adducts. The changes in the half-width for the polarization components of various Raman bands on the transition from the orthorhombic to the hexagonal phase are interpreted in terms of the correlation functions of the Raman tensor related to the rotational motion of the alkane molecules around the chain axes.     

Detection of methotrexate in a flow system using electrochemical impedance spectroscopy and multivariate data analysis

Methotrexate (MTX), a common pharmaceutical drug in cancer therapy and treatment of rheumatic diseases, is known to cause severe adverse side effects at high dose. As the side effect may be life threatening, there is an urgent need for a continuous, bed-side monitoring of the nominal MTX serum level in a patient while the chemical is being administered. This article describes a detection of MTX using a flow system that consists two modified gold electrodes. Interaction of MTX with the antibodies fixed on the electrode surface is detected by electrochemical impedance spectroscopy and evaluated using singular value decomposition (SVD). The key finding of this work is that the change in the electrode capacitance is found to be quantitative with respect to the concentration of MTX. Moreover a calibration curve constructed using the principal component regression method has a linear range of six orders of magnitude and a detection limit of 1.65 × 10⁻¹⁰ M.     

Static and dynamic structural properties of macromolecules from FT vibrational spectroscopy

The problem of the normal vibrations of pinned and mobile conformational defects in polymethylene chains is presented and discussed from the viewpoints of theory and experiments. A few cases in technologically relevant materials are presented in which FT-IR and FT-Raman spectroscopy provide unique detailed information which could not be obtained before with any other technique.     

Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification

This paper proposes an analytical method to detect adulteration of diesel/biodiesel blends based on near infrared (NIR) spectrometry and supervised pattern recognition methods. For this purpose, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) coupled with the successive projections algorithm (SPA) have been employed to build screening models using three different optical paths and the following spectra ranges: 1.0 mm (8814-3799 cm⁻¹), 10 mm (11,329-5944 cm⁻¹ and 5531-4490 cm⁻¹) and 20 mm (11,688-5952 cm⁻¹ and 5381-4679 cm⁻¹). The method is validated in a case study involving the classification of 140 diesel/biodiesel blend samples, which were divided into four different classes, namely: diesel free of biodiesel and raw vegetal oil (D), blends containing diesel, biodiesel and raw oils (OBD), blends of diesel and raw oils (OD), and blends containing a fraction of 5% (v/v) of biodiesel in diesel (B5). LDA-SPA models were found to be the best method to classify the spectral data obtained with optical paths of 1.0 and 20 mm. Otherwise, PLS-DA shows the best results for classification of 10 mm cell data, which achieved a correct prediction rate of 100% in the test set.     

Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900–1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R²) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat.